title: Predicting free-living energy expenditure using a miniaturized ear-worn sensor: an evaluation against doubly labeled water.
abstract: Accurate estimation of daily total energy expenditure (EE)is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions.An EE prediction model as first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation(MAD) below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p 0.008, and a MAD of 272 kcal day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions [corrected].
authors.forename: Loubna
authors.initials: L
authors.lastname: Bouarfa
authors.forename: Louis
authors.initials: L
authors.lastname: Atallah
authors.forename: Richard Mark
authors.initials: RM
authors.lastname: Kwasnicki
authors.forename: Claire
authors.initials: C
authors.lastname: Pettitt
authors.forename: Gary
authors.initials: G
authors.lastname: Frost
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000328:Adult; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D055686:Metabolic Equivalent / Q000502:physiology; D008904:Miniaturization / Q000295:instrumentation*; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted